Intelligent workflow automation is more than the sum of its parts. AI and automation together create a better effect than either component could alone. Yet the magic ingredient is arguably AI.
With the rise of AI, enterprise-scale firms have been rushing to integrate it into their workflows – 38%, according to an IBM Survey. Adding AI seems to be the missing ingredient in traditional workflows. AI’s adaptability and speed have upstaged traditional Robotic Process Automation (RPA) and Business Process Management (BPM)-based workflow technologies. Now, workflow automation is adaptable and can prevent the recurrence of errors.
While AI workflow automations are theoretically easy to understand, it can be challenging to visualise the practical applications. So let’s break it down.
According to IBM, ‘a workflow is a system for managing repetitive processes and tasks which occur in a particular order.’ By disassembling processes into a series of mapped steps, businesses can automate processes to save time and costs. Though you can use workflows for any value-generating activity, AI-powered workflows are particularly well-suited for information processing due to their speed and precision.
The word ‘intelligent’ indicates the presence of AI-powered algorithms. For example, intelligent data extraction refers to AI-powered data extraction.
Of course, the AI must accomplish a specific function within the workflow. We’ve written before about the dangers of AI washing – where a product or service is falsely labelled as ‘AI-based’ or ‘AI-powered’. Many businesses cash in on the hype surrounding AI, only to overstate the sophistication of their products.
AI algorithms should imbue a workflow with flexibility or the ability to adapt to contextual changes.
The idea behind intelligent workflow automation is that it should be as hands-off as possible. Rather than feeding in documents or manually changing its settings, the workflow should automatically adapt to unexpected situations.
Here’s an example of how to establish an intelligent workflow and how it will operate.
You must establish a clear structure and decision-making process to create an effective workflow. At the start of the decision-making process, you must define the workflow’s trigger – or the ‘starting point’. Then, the workflow should integrate decision points (i.e. where the data should be transferred, whether it should be validated, etc.) and all the potential outcomes.
Then, it’s a matter of identifying the right automation strategy to form the workflow’s technological backbone. For intelligent workflow automation, AI algorithms will help orchestrate the process. They’ll be able to organise a context-appropriate response in unexpected situations (such as data surges, system failures, data breaches and more).
Firm exception-handling controls ensure unexpected errors don’t disrupt the workflow. It is unrealistic to expect that errors will never occur in a workflow. Instead, what is realistic is that the system resolves them quickly and prevents their recurrence.
Effective exception handling incorporates error detection, classification based on severity and error handling procedures (such as retrying, rollback and escalation). An intelligent workflow should then ideally introduce measures such as data validation rules or automated error checks that prevent the error from occurring in the future.
Rigorous testing is the only way to validate a system’s robustness. Deploy the workflow in a production environment and adapt as appropriate. Then, your intelligent automation workflow is ready to be released.
Of course, these workflow steps might seem vague without a concrete application. So, let’s explore a common use case of intelligent workflow automation: document management in financial services.
Financial services is a notoriously document-heavy industry, with document-intensive processes such as loan origination, anti-money laundering (AML) and insurance claim filings. Some of these processes, like loan origination, can involve up to 100 different documents being processed.
Automation offers a cost-friendly technology solution for circumventing inevitable human error – while saving hours of labour. In other words, instead of getting humans to pore over densely structured and populated financial documents, automation presents a quicker alternative.
Here’s how a document like an invoice might flow through an AI-powered document workflow.
Examples of these triggers could be:
The second step of a document automation workflow generally involves extracting relevant data. You can then use the extracted data for analysis further down the workflow.
The AI algorithms should read the invoice quickly, identifying the data points as a human would. The data is then available for downstream processing. But first, it must be validated.
Data quality must be validated before being deployed in company processes, with mechanisms assessing it by checking for internal consistency. Additionally, they measure data against internal benchmarks to identify anomalies and potentially verify data across different systems.
The next steps are optional but may involve identification and matching.
AI algorithms will then review the invoice’s format to classify its type (i.e. receipt, purchase order, debit note, credit memo and so on). The invoice is then matched to a vendor in the company’s database by matching the information and preparing it for reconciliation.
The workflow might then enrich the data. For example, the currency from the invoices might be converted into the company’s base currency. The data could also be infused with third-party sources to add customer information. At the end of the enrichment stage, the information should be fully actionable.
Another optional workflow step is producing analytics. The system may analyse the invoice data to identify trends and cost-saving opportunities while flagging potential payment issues.
The algorithms may also be able to produce metadata about key performance indicators (KPIs), such as average invoice processing time, payment cycle time and invoice error rates.
Ultimately, this entire workflow should take seconds and incur low costs. The AI-powered system’s processing power should be negligible.
Though intelligent automation workflows offer convenience and cost savings for companies of all sizes, they offer nuances you’ll want to consider alongside their benefits.
In 2021, Knight Capital lost $440 million (three times its monthly earnings) in 45 minutes. A glitch in its trading algorithms caused the firm to send millions of attached orders, resulting in the execution of millions of trades across numerous stocks in less than an hour. The company did not survive the incident.
The glitch in Knight Capital’s software raises concerns about the necessity of resilience and transparency in automated processes. Knight Capital’s case is one of the most egregious, but it’s only one of many incidents where unprecedented software glitches resulted in disproportionate consequences.
So, you’ll always want to avoid cutting corners and settling for speed without robustness. One way to test the resilience of the workflow is through scenario modelling. If you change the conditions of your automated workflow, you can monitor how the workflow changes. During periods of economic uncertainty, relying on your systems is essential.
With projects like intelligent workflow automation, scope creep may occur. Trying to alter the platforms to gain increasingly sophisticated outputs is tempting. For example, you might add functionalities like sentiment analysis or other analytics that do not match the original project goals.
Project management issues like scope creep, drive up costs and mean implementing intelligent workflow automation takes longer than it should. Other ways to maintain costs are:
If well-executed, intelligent workflow automation can save substantial costs in the long term and only takes weeks or even days to complete.
Though the future of intelligent workflow automation is largely unknown, there are some directions it could take:
To summarise, the future of intelligent workflow automation is uncertain. Depending on how developers and enterprises utilise the technology, its impact could be positive or negative.
Establishing an automated, AI-powered workflow may seem like a mammoth project with many intricacies. But, by combining a holistic approach with attention to detail, intelligent workflow automation could be a good investment for your firm.
When speaking with vendors, consider asking questions like:
Evolution AI has worked with businesses like Novuna Business Finance and NatWest to install AI-powered document automation workflows. Our workflows have saved 75% of costs and 95% of document processing time – with complete accuracy.
Interested in learning more? Speak to our financial data project team by booking a demo. Or email hello@evolution.ai for more information.
